Intelligent Control

Intelligent control aims to develop systems that autonomously adapt and optimize their actions in complex and dynamic environments. Current research emphasizes integrating advanced machine learning techniques, such as reinforcement learning and neural networks (including those leveraging quantum computing principles), with established control strategies like sliding mode control and fuzzy logic, often within hybrid architectures combining symbolic reasoning and data-driven learning. These advancements are improving the efficiency and robustness of control systems across diverse applications, from robotic manipulation and industrial automation to optimizing transportation networks and managing complex industrial processes.